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Financial time series forecasting using empirical mode decomposition and support vector regression

Author

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  • Nava, Noemi
  • Di Matteo, Tiziana
  • Aste, Tomaso

Abstract

We introduce a multistep-ahead forecasting methodology that combines empirical mode decomposition (EMD) and support vector regression (SVR). This methodology is based on the idea that the forecasting task is simplified by using as input for SVR the time series decomposed with EMD. The outcomes of this methodology are compared with benchmark models commonly used in the literature. The results demonstrate that the combination of EMD and SVR can outperform benchmark models significantly, predicting the Standard & Poor’s 500 Index from 30 s to 25 min ahead. The high-frequency components better forecast short-term horizons, whereas the low-frequency components better forecast long-term horizons.

Suggested Citation

  • Nava, Noemi & Di Matteo, Tiziana & Aste, Tomaso, 2018. "Financial time series forecasting using empirical mode decomposition and support vector regression," LSE Research Online Documents on Economics 91028, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:91028
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    References listed on IDEAS

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    Cited by:

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    3. Flavio Barboza & Geraldo Nunes Silva & José Augusto Fiorucci, 2023. "A review of artificial intelligence quality in forecasting asset prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1708-1728, November.
    4. Tim Leung & Theodore Zhao, 2021. "Multiscale Decomposition and Spectral Analysis of Sector ETF Price Dynamics," JRFM, MDPI, vol. 14(10), pages 1-22, October.
    5. Tim Leung & Theodore Zhao, 2022. "Adaptive complementary ensemble EMD and energy-frequency spectra of cryptocurrency prices," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 9(01), pages 1-23, March.
    6. Tim Leung & Theodore Zhao, 2021. "Financial Time Series Analysis and Forecasting with HHT Feature Generation and Machine Learning," Papers 2105.10871, arXiv.org.
    7. Wenting Zhao & Juanjuan Zhao & Xilong Yao & Zhixin Jin & Pan Wang, 2019. "A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand," Energies, MDPI, vol. 12(7), pages 1-28, April.
    8. Dionne, Georges & Koumou, Gilles Boevi, 2018. "Machine Learning and Risk Management: SVDD Meets RQE," Working Papers 18-6, HEC Montreal, Canada Research Chair in Risk Management.

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    More about this item

    Keywords

    empirical mode decomposition; support vector regression; forecasting;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G2 - Financial Economics - - Financial Institutions and Services

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